Intracranial EEG reveals a time- and frequency-specific role for the right inferior frontal gyrus and primary motor cortex in stopping initiated responses

Nicole Swann, Nitin Tandon, Ryan Canolty, Timothy M Ellmore, Linda K McEvoy, Stephen Dreyer, Michael DiSano, Adam R Aron, Nicole Swann, Nitin Tandon, Ryan Canolty, Timothy M Ellmore, Linda K McEvoy, Stephen Dreyer, Michael DiSano, Adam R Aron

Abstract

Inappropriate response tendencies may be stopped via a specific fronto/basal ganglia/primary motor cortical network. We sought to characterize the functional role of two regions in this putative stopping network, the right inferior frontal gyrus (IFG) and the primary motor cortex (M1), using electocorticography from subdural electrodes in four patients while they performed a stop-signal task. On each trial, a motor response was initiated, and on a minority of trials a stop signal instructed the patient to try to stop the response. For each patient, there was a greater right IFG response in the beta frequency band ( approximately 16 Hz) for successful versus unsuccessful stop trials. This finding adds to evidence for a functional network for stopping because changes in beta frequency activity have also been observed in the basal ganglia in association with behavioral stopping. In addition, the right IFG response occurred 100-250 ms after the stop signal, a time range consistent with a putative inhibitory control process rather than with stop-signal processing or feedback regarding success. A downstream target of inhibitory control is M1. In each patient, there was alpha/beta band desynchronization in M1 for stop trials. However, the degree of desynchronization in M1 was less for successfully than unsuccessfully stopped trials. This reduced desynchronization on successful stop trials could relate to increased GABA inhibition in M1. Together with other findings, the results suggest that behavioral stopping is implemented via synchronized activity in the beta frequency band in a right IFG/basal ganglia network, with downstream effects on M1.

Figures

Figure 1.
Figure 1.
The conditional stop-signal task used in the electrocorticography experiment. Each trial begins with a cue circle (with, for example, the left half red, shown here as black, and the right half green, shown here as gray). The cue reminds the participant that the “critical” direction (red) is the left response and the “noncritical” direction (green) is the right response. The rules stay the same for the entire session. On go trials, the participant has 1 s (the hold period) to press the left or right button in response to a stimulus. On a stop trial, a tone is played at some delay (SSD) after the arrow stimulus. The SSD changes dynamically throughout the experiment (increasing or decreasing by 50 ms depending on whether the participant stopped or not on prior stop trials). If the arrow stimulus is in the critical direction and a tone occurs, then the subject must try to stop, but if the arrow is in the noncritical direction and a tone occurs then the subject must respond anyway.
Figure 2.
Figure 2.
Location of intracranial grids on the right hemisphere. For each patient the ECoG grid is registered to the patient's own structural MRI. For the patients for whom we report a right IFG response, the probabilistically determined right IFG region is outlined in black (Eickhoff et al., 2005). For the patients for whom we report M1 results, the hand area of M1, determined by a neurosurgical expert, is outlined in blue. IFC, IFG cortex.
Figure 3.
Figure 3.
ECoG analysis procedure. (1) Raw EEG signal (after referencing to common average). (2) Filtering. The raw signal is filtered using a gabor wavelet technique into many separate frequencies (only 3 are shown for visualization purposes). (3) Deriving the analytic signal. Traces of the filtered signal for three representative frequencies are shown in black. The red line shows the analytic amplitude (“power”) for each of these frequencies. It is this analytic value over time for each frequency that is used for the remainder of the analysis. (4) Epoching. All events of one trial type, e.g., successful (Suc.) stop, are grouped together. The analytic signals across the length of the trial are averaged together at each particular frequency. The panel shows one frequency band for illustration.
Figure 4.
Figure 4.
Correspondence between fMRI and ECoG for patient TA341. Functional MRI activation represents the contrast successful stop–go (red). The time–frequency plot (bottom left) shows ECoG data from one electrode (marked in black on the MRI) for successful stop trials. Zero ms is the time of the stop signal. Right, Beta power over time for this and several other electrodes in the region. The color of each trace corresponds to the color of the electrode shown on the structural MRI. The electrode marked in black was selected for further analysis and had both the closest spatial correspondence to right IFG fMRI activation and also the strongest response (z score ∼6). [Note that the other electrodes in the IFG were excluded because of electrical contamination.]
Figure 5.
Figure 5.
Right IFG results for ECoG. The figure shows a stronger ECoG response for successful versus unsuccessful stop trials. Data are shown for three patients, and for one patient there were 2 d (sessions). Zero milliseconds is the time of the stop signal, indicated by a thick vertical black line. The first two columns show data from each condition relative to the baseline period as a z score. The third column shows the difference between conditions (relative to one another, not to the baseline), also as a z score. The fourth column shows the same data with power averaged across the beta band (13–18 Hz), plotted for both conditions over time (red line indicates successful stopping; blue line indicates unsuccessful stopping). In all cases there is a beta increase (∼16 Hz) that is larger for successful than for unsuccessful stop trials. The thin black outlines in columns 1 and 2 indicate p < 0.01, FDR corrected. The thin red outlines shown in the difference column indicate p < 0.05, uncorrected. The dotted horizontal line marks 16 Hz for all patients. The bottom row shows the average of z scores across patients, obtained by averaging the average z scores for each patient, down-sampled to 100 Hz to be comparable across patients.
Figure 6.
Figure 6.
A representative electrode from auditory cortex. This shows a very different pattern of response for successful versus unsuccessful stop trials compared to right IFG. These data are from one electrode for patient, TS007 (for other patients, see supplemental Fig. 2, available at www.jneurosci.org as supplemental material). No substantial, statistically significant differences are present for the contrast of these event types (see difference plot). The event-related potential (low-pass filtered at 40 Hz) shows an almost exactly overlapping response between conditions, which begins very shortly after the stop signal (<50 ms).
Figure 7.
Figure 7.
Right primary motor cortex results for ECoG. There is a different ECoG response for successful versus unsuccessful stop trials. Zero milliseconds is the time of the go signal, indicated by a thick vertical black line. The first two columns show data from each condition relative to the baseline period (intertrial interval) as a z score. The third column shows the difference between conditions (relative to one another, not to the baseline), also as a z score. The fourth column shows average alpha/beta power (8–30 Hz) over time. As for Figure 5, thin black outlines indicate p < 0.01, FDR corrected; thin red outlines in the difference column indicate p < 0.05, uncorrected; dotted horizontal line marks 16 Hz for all patients. The bottom row shows the average of z scores across patients. Responses were made with the left hand.

Source: PubMed

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